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Volatile Biomarkers for Aquatic Ecological Research
CHAPTER THREE
Volatile Biomarkers for Aquatic Ecological Research Michael Steinke1, Luli Randell, Alex J. Dumbrell, Mahasweta Saha2 University of Essex, School of Biological Sciences, Colchester, United Kingdom 1 Corresponding author: e-mail address: [email protected]
Contents 1. Introduction 1.1 Volatilomes: The Volatile Subset of Metabolomes 1.2 Volatilomics: Terms and Techniques 2. Principal Techniques for Measuring Biogenic Volatiles 3. Medical Volatilomics Provides a Blueprint for Ecological Research 4. Role of Volatiles in Aquatic Ecological Interactions 4.1 Case Study: Volatilomes of Freshwater Phytoplankton 5. Application of Volatilomics to Ecological Research: Using Volatilomics to ‘Direct’ Environmental Management Acknowledgements References
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Abstract All organisms and ecosystems emit and consume volatile organic compounds (VOCs). Traditionally, these have been qualitatively and quantitatively described in isolation without full consideration of the ‘signatures’ produced by the totality of all volatiles released. Here, we suggest that volatilomics, a research area applied to medical diagnostics, soil biology and pest control, can advance aquatic ecological research by providing a relatively fast diagnostic tool to investigate, for example, taxonomic and likely also functional diversity in aquatic systems—providing a novel technique for the biomonitoring of aquatic environments. Our case study demonstrates the utility of volatilomics to differentiate between four different algal genera using a principal component analysis. We highlight the utility of volatilomics to the monitoring of environmental processes and discuss its application to inform industrial mariculture procedures. Keywords: Volatilomics, Volatile organic compounds (VOCs), Biomonitoring, Aquatic, Environmental management 2
Current address: GEOMAR Helmholtz Centre for Ocean Research Kiel, D€ usternbrooker Weg 20, 24105 Kiel, Germany.
Advances in Ecological Research, Volume 59 ISSN 0065-2504 https://doi.org/10.1016/bs.aecr.2018.09.002
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2018 Elsevier Ltd All rights reserved.
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1. INTRODUCTION Terrestrial and aquatic plants and animals produce volatile organic compounds (VOCs) that provide signals acting as chemical cues, playing central roles in the recognition, attraction and deterrence of different species. Aquatic organisms emit volatiles when exposed to air or submerged underwater for intra- and interspecific communication (Mollo et al., 2017; Saha et al., 2018). ‘Volatilomics’ refers to the scientific study of volatile metabolites and, although relatively new in the ‘omics’ arena, is increasingly appreciated as a tool for the noninvasive presymptomatic diagnosis of human health (Amann and Smith, 2013). This has resulted in new methods for the early detection of various diseases in humans and can critically assist with identifying suitable strategies in the management of human health. Although technically feasible, similar applications have not yet been developed in aquatic research, yet these have the potential to be employed as a noninvasive, nondestructive, rapid and potentially highly cost-effective tool for biomonitoring, allowing users to readily survey the health status of marine and freshwater organisms and their environments. Given the embryonic stage of volatilomics research in being applied to aquatic biomonitoring, here we provide a brief overview of this developing technique and discuss the utility of aquatic volatilomics as a tool to advance freshwater and marine ecological research.
1.1 Volatilomes: The Volatile Subset of Metabolomes Every organism produces metabolites that provide unique chemical footprints. The scientific study of these metabolites is referred to as metabolomics and aims to describe the diversity and abundance of metabolites in samples of differing complexities of their taxonomic composition, inter- and intraspecific interactions (e.g. sexual attraction, predator deterrence) or sampled environment (e.g. laboratory culture, microcosm or ecosystem). Hence, metabolomics offers a powerful approach to represent the link between genotype and molecular phenotype, and can be used to elucidate functions from the organism to the ecosystem scale (Goulitquer et al., 2012). For example, specific metabolomic signatures can be used to reveal both an organism’s presence and its metabolic activity in the environment (Lee et al., 2012). The ‘volatilome’ represents the volatile subset of the metabolome, and its study finds application for the development of noninvasive biomarkers in the
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clinical diagnosis and monitoring of human diseases (Amann and Smith, 2013). It is also used in pest control and yield improvement in agricultural systems (Pickett and Khan, 2016; Shrivastava et al., 2010) and sometimes focuses on the microbial production of volatiles in soils to characterise the function of plants, fungi and bacteria (Insam and Seewald, 2010; Kanchiswamy et al., 2015; Redeker et al., 2018).
1.2 Volatilomics: Terms and Techniques Various terms are used to describe and classify volatiles (Achyuthan et al., 2017), and examples are presented in Table 1. Compounds in these classes are characterised by a relatively high vapour pressure at room temperature so that molecules of volatile compounds have a large tendency to escape from their liquid or solid form into the gas phase. Terms used to describe (components of ) the volatilome include volatile inorganic compounds (VICs), biogenic volatile inorganic compounds (BVICs), volatile organic compounds (VOCs), nonmethane volatile organic compounds (NMVOCs), biogenic volatile organic compounds (BVOCs), microbial volatile organic compounds (MVOCs), oxygenated volatile organic compounds (OVOCs), hydrocarbons (HCs) and nonmethane hydrocarbons (NMHCs). There is a confusing use of terms in the literature that describe volatiles released in biological processes. The three principle terms ‘volatomics’, ‘volatolomics’ and ‘volatilomics’ (and their derivatives ‘volatome’, ‘volatolome’ and ‘volatilome’) are currently used in the literature to describe the study of volatiles produced in biological processes. A search on the Web of Science for ‘volatom*’, ‘volatolom*’ and ‘volatilom*’ showed that the terms volatilomics and volatilome are most widely used in the published literature (559 citing articles, h-index of 12; 18 July 2018). However, here we briefly consider the meaning and definitions of these neologisms in an attempt to avoid possible confusion and encourage a coherent use of appropriate terms in the future. The ‘volatome’ is defined as ‘… the sum of all released volatile organic compounds (VOCs) over a specific time and space…’ (D’Alessandro, 2006). By this definition, the term excludes inorganic volatiles (e.g. H2S) that may add important diagnostic potential when studying the trace gas biology of aquatic environments. Furthermore, from a linguistic point of view, ‘volatomics’ may be confused with ‘something to do with atoms’ and we suggest that volatomics may be not the most appropriate term to use when describing the release of volatiles from biological systems.
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Table 1 Classes of Volatiles With Typical Abbreviations, Their Definition and Example Compounds Typical Class Abbreviation Definition Compound(s) References
Volatile inorganic compound
VIC
Carbon-free compound that has a high vapour pressure at room temperature
Argon, helium, Kennes et al. nitrogen, hydrogen (2016) sulphide, ammonia
Biogenic volatile inorganic compound
BVIC
Volatile inorganic compound produced directly but not exclusively from biological processes
Nitrogen, hydrogen sulphide, ammonia
Kennes et al. (2016)
Volatile organic compounds
VOC
Carbon-containing compound that has a high vapour pressure at room temperature
Methane, acetone, dimethyl sulphide
Krupa and Fries (1971)
Nonmethane NMVOC volatile organic compound
All VOCs but excluding methane
Acetone, dimethyl sulphide
Anastasi et al. (1991)
Biogenic volatile organic compounds
BVOC
Volatile organic compound produced directly but not exclusively from biological processes
Hydrogen sulphide, dimethyl sulphide, isoprene, ethene
Geron et al. (1994)
Microbial volatile organic compounds
MVOC
Volatile organic compound formed in the metabolism of fungi and bacteria (preferentially used in soil ecology literature)
Geosmin, dimethyl disulphide, hydrogen cyanide
Korpi et al. (2009)
Oxygenated volatile organic compounds
OVOC
Variety of volatile organic compound with oxygenated side groups
Methanol, acetone
Heikes et al. (2002)
Hydrocarbons HC
Organic compound consisting entirely of hydrogen and carbon
Methane, pentane, ethene
Nonmethane NMHC hydrocarbons
All hydrocarbons excluding methane
Butane, isoprene
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As far as we are aware, a clear definition of ‘volatolome’ is lacking in the literature. The term suggests a closer relationship with ‘metabolome’ and may be the most appropriate term when specifically addressing the volatile metabolome, i.e., the volatile subset of organic and inorganic metabolites produced by the collective metabolism(s) of organism(s), communities or entire ecosystems. It is important to note that a volatolome is the net result of volatile production and consumption processes. The term ‘volatilome’ includes the volatolome plus nonbiogenic, exogenously derived compounds that do not stem from metabolic processes (e.g. environmental contaminants; Insam and Seewald, 2010). For example, this term is often used in the medical study of human breath where the volatile composition is influenced by the combination of metabolically produced gases and volatiles exchanged with the atmosphere or ingested with food. Therefore, volatilome may be the best general term to use when the metabolic processes leading to the production of volatiles are unknown or poorly characterised. Measurements of the volatilome can provide an in vivo metabolic footprint of the entirety of volatiles released without sample pretreatment or extraction (Insam and Seewald, 2010). In many settings, modern volatilomics may be superior to other biomarkers since it offers the possibility for an immediate, continuous (online) measurement which applies, for example, to quality control (e.g. food spoilage, mould detection; Mayr et al., 2003), biodiversity assessment (e.g. herbivore detection; Miresmailli et al., 2010) and medical diagnostics (see Section 3). It is also clear that individual components in environmental volatilomes are of value as informationconveying chemicals (infochemicals) in ecological research (see Section 4). This is because they are central to regulating (i) individuals’ movement and behaviour, (ii) ecological interactions between and across populations and (iii) have the potential to affect the complexity of trophic structure in marine food webs (Nevitt et al., 1995; Steinke et al., 2002b, 2006).
2. PRINCIPAL TECHNIQUES FOR MEASURING BIOGENIC VOLATILES A number of techniques used in volatilomic analyses have recently been reviewed by Achyuthan et al. (2017). Most analyses use a type of gas chromatography (GC) coupled to various detectors including, for example, mass spectrometric detection (GC-MS; Hopkins et al., 2010), flameionisation detection (GC-FID; Steinke et al., 2018), flame-photometric
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detection (GC-FPD; Steinke et al., 2002a), photoionisation detection (GC-PID; Zhou et al., 2013) or Fourier transform infrared spectroscopy (GC-FTIR; Ketola et al., 2006). These techniques can be further combined with microextraction techniques including headspace solid-phase microextraction (HS-SPME; Vogt et al., 2008) or direct-immersion solid-phase microextraction (DI-SPME; Zhang et al., 2018) and may be extended to include two-dimensional separation techniques (e.g. GC GC-TOF MS; Phillips et al., 2013). GC techniques use various column materials to separate individual volatiles in often complex mixtures, and the choice of column material and methods in general is selective for particular groups of volatiles. This suggests (i) that the choice of methodological approach strongly affects possible outcomes of a study and (ii) that GC analysis is typically incapable of identifying and quantifying the total volatilome. Other spectrometric methods may overcome such limitations: for example, proton-transfer reaction mass spectrometry (PTR-MS; Halsey et al., 2017; Mayr et al., 2003) and chemical ionisation mass spectrometry (CIMS; Hopkins et al., 2016) have sufficient sensitivity to allow continuous monitoring (online measurement) of selected gas samples.
3. MEDICAL VOLATILOMICS PROVIDES A BLUEPRINT FOR ECOLOGICAL RESEARCH Research in medical volatilomics has greatly advanced the application of biogenic volatiles for the rapid and noninvasive diagnosis of early bacterial infections and genetic disorders including cancer or Alzheimer’s disease (Amann et al., 2014). Since metabolic reactions in cells, tissues or fluids result in the formation of volatiles, physiological abnormalities can be detected using methods that identify and quantify volatiles in urine, faeces or exhaled breath samples. For example, GC-MS analysis of urine samples demonstrates unique volatile compounds in transgenic mice with mutations on the amyloid precursor protein gene involved in the onset of Alzheimer’s disease (Kimball et al., 2016). In patients suffering from cystic fibrosis (CF), secondary infections with Pseudomonas aeruginosa alter microbial diversity and produce a change in a patient’s breath volatilome. This offers monitoring opportunities for airway infection as a responsive tool to reduce mortality rates in CF patients (Robroeks et al., 2010). HS-SPME coupled to GC-MS identified six compounds (2-pentanone, 2-heptanone, 3-methyl-3-buten-1-ol, ethyl acetate, ethyl propanoate and 2-methyl butanoate) that were only found in the headspace of cancerous cell lines (Silva et al., 2017). This knowledge can inform the development of novel technologies including GC-MS coupled
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to gold nanoparticle sensor arrays to distinguish between breath samples of cancer patients and healthy control groups (Peng et al., 2010). We suggest that much of the existing technology used in medical research can readily inform the investigation of ecological processes. Volatile-mediated signalling in plants already finds application in agriculture (Pickett and Khan, 2016), whereas ecological research using volatiles in aquatic environments is in its infancy (Saha et al., 2018). For example, identifying substantial differences in the quantitative and/or qualitative differences in volatilomes of ecosystems before and during disturbance would accelerate developing robust sensor technology for ecosystem biomonitoring. This could provide novel fast, sensitive and relatively inexpensive tools to alert of an onset of disturbance including infestation with parasites (e.g. salmon lice infection in finfish mariculture) or the changes associated with the deterioration of symbiotic relationships (e.g. before and during coral bleaching). Such early warning would provide avenues for new management strategies that prevent or reduce the degradation of ecosystems. For example, early detection of salmon lice may minimise the geographic spread of infestations so that costly and environmentally damaging treatments can be optimised. Another example application could allow for combinations of coral and their symbionts to be manipulated and tested in coral restoration projects to identify holobionts with greater resistance to stress responses triggered by environmental change.
4. ROLE OF VOLATILES IN AQUATIC ECOLOGICAL INTERACTIONS Volatiles shape a myriad of species interactions on land and critical ecological processes that depend on smell are commonplace (Ache and Young, 2005; Kessler and Baldwin, 2001). For example, volatiles often facilitate communication through their role in intra- and interspecific signalling in bacteria and fungi (e.g. hydrogen cyanide produced in bacteria; Piechulla et al., 2017), plants (e.g. the gaseous hormone ethylene; Wang et al., 2002), insects (e.g. trail-marking in ants, sex pheromones in moths; Wyatt, 2014) and vertebrates including humans (Nevitt, 2008; Wallraff, 2004; Wyatt, 2014). In contrast to the terrestrial environment, our understanding of volatilemediated processes in aquatic environments is limited, with previous studies restricted to a relatively small number of infochemicals (Giordano et al., 2017; Hay, 2009; Moelzner and Fink, 2015; Pohnert et al., 2007). This is surprising since aquatic environments are of particular relevance for volatile-mediated infochemistry with diffusion typically four orders of
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magnitude slower in water than in air. Furthermore, numerous microbial consumption processes, as exemplified for the abundant trace gases dimethyl sulphide (DMS; Sch€afer et al., 2010) and isoprene (Alvarez et al., 2009), rapidly decrease background concentrations and substantially enhance the directional quality of chemical gradients providing the basis of efficient chemical communication. Hence, diffusive volatiles are among the most important parts of the ‘chemical language’ in the infochemistry of any aquatic organism as demonstrated by the identification of the volatile sexual pheromone ectocarpene in the seaweed Ectocarpus siliculosus about 30 years ago (Maier and M€ uller, 1986). Aquatic volatiles can also transfer into the atmosphere to communicate food web interactions over relatively long distances (Nevitt et al., 1995; Savoca and Nevitt, 2014) and affect atmospheric processes and climate (Carpenter et al., 2012). For example, DMS, isoprene and numerous halocarbons are climatically important gases that have received global research attention and have been studied for their importance to ecological (Steinke et al., 2006), biogeochemical (Hopkins et al., 2010) and climate science (Vallina and Simo´, 2007). Albatrosses and petrels rely on the volatile sulphur gas DMS to track highly productive areas (Nevitt, 2008), and brown seaweeds release volatile pheromones (Pohnert and Boland, 2002). Volatiles also react quickly in response to abiotic conditions and climate stressors (Pen˜uelas and Staudt, 2010). For example, the production of toxic cyanogen bromide (BrCN) by the microalgae Nitzschia cf. pellucida that kills surrounding biofilm organisms is light dependent with a short burst of BrCN immediately after sunrise (Vanelslander et al., 2012). Furthermore, different VOCs are emitted by healthy, senescent or stressed cells, apoptotic tissue or prey under predator attack (Achyuthan et al., 2017). These evidences provide us with a ‘smoking gun’ and make VOCs strong candidates for the interrogation of the state of health of aquatic organisms or communities, and thus the quantification of the volatilome as an ideal target for longer-term biomonitoring of aquatic ecosystems. To explore the basic functionality of volatilomics for biodiversity research, we reanalysed a dataset on freshwater volatiles and demonstrate proof of principle.
4.1 Case Study: Volatilomes of Freshwater Phytoplankton We previously conducted measurements of isoprene and DMS in unialgal cultures of freshwater phytoplankton (Steinke et al., 2018). Using the raw gas chromatographic data, we explored the diversity of algal volatilomes
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in four species from different taxonomic classes: Chlamydomonas reinhardtii (Chlorophyceae), Cryptomonas sp. (Cryptophyceae), Cyclotella meneghiniana (Bacillariophyceae) and Aphanizomenon flos-aquae (Cyanophyceae). Method details are described in Steinke et al. (2018). Briefly, algal cultures were grown under constant conditions using culture-specific media, aliquoted into glass bottles that were closed with gas-tight glass stoppers (Winkler bottles) and incubated under growth conditions for approximately 4 h. At the end of the incubation, media were purged with N2 gas at 80 mL/min and this sample gas stream was cryogenically enriched at 150°C before analysis using gas chromatography with flame ionisation detection (GC-FID). The diversity and abundance of peaks on the chromatograms indicated speciesspecific release of volatiles from the cultures (Fig. 1). The chromatogram traces were manually aligned for comparison, and the data deconstructed into a multivariate matrix on which principal component analysis (PCA) was applied to identify distinct data clusters associated with the four algal genera (Fig. 2). With this limited dataset it may be premature to attempt an in-depth assessment of the suitability of volatilomics for algal research. For example, it remains to be tested whether the quantity of volatiles is directly proportional to algal biomass and how fluctuations in environmental conditions may affect the quantity and quality (plasticity) of volatilomes to identify robust volatile biomarkers for species of interest. Furthermore, it is also possible that composition and age of algal media may affect the outcome of volatilomic analyses. To counter this, algal media should be taken as controls to eliminate background noise. Nevertheless, the data suggest that the quantification of volatiles could be explored for quality control in algal biotechnology and may be useful in the assessment of biodiversity in complex mixtures and environmental samples. It is important to note that environmental volatilomics should not be confused with an overly simplistic ‘one signature compound per species or function’ approach but uses the entirety of the volatile footprint to inform how we describe biodiversity. The separation of thousands of volatile compounds is already feasible in human breath analysis (e.g. Phillips et al., 2013), and tens of thousands of molecular formulae can be identified in complex natural organic matter using Fourier transform ion cyclotron resonance mass spectra (FT-ICR-MS; Riedel and Dittmar, 2014). As already applied to other omics approaches, a volatilomic assessment of complex environmental samples would require the collection of large datasets in publicly accessible databases, preferentially using unified methodological approaches (see Section 5).
Fig. 1 Overlays of typical chromatograms for volatile organic compounds (VOC) produced from similar biomass in freshwater phytoplankton. (A) Overlay of data for Cyclotella sp. (brown line) and instrument blank (grey line) showing numerous signature peaks. Grey rectangles indicate position of timeshots in lower panel. (B–D) Overlays showing examples of selected signature volatiles in four species that illustrate the rich signature qualities for the identification of specific genera (Chlamydomonas (green line), Cyclotella (brown line), Cryptomonas (red line), Aphanizomenon (blue-green line)), based on their volatile profiles. Grey line shows instrument blank.
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Principal component dimension two (18%)
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Aphanizomenon sp. Cyclotella sp.
2 0 −2 −4
Chlamydomonas sp. Cryptomonas sp. 0 5 −5 Principal component dimension one (27%)
Fig. 2 Principal component analysis (PCA) of volatilomes from four specific genera (Chlamydomonas, Cyclotella, Cryptomonas, Aphanizomenon) demonstrates clear separation based on taxonomic identity of the unialgal cultures.
Once the volatilome patterns of major components to biodiversity are known, it will become possible to search for such patterns and identify key volatilome components for possible monitoring purposes. For example, if we demonstrate that harmful algal bloom species produce typical volatile footprints, a network of simple sensors that target a low number of volatiles may inform established but costly routine sampling that uses either microscopic enumeration or genetic identification of harmful species. Our case study illustrates the utility of volatilomics to investigate taxonomic diversity; however, the production of volatiles is also affected by functional diversity. Microcosm experiments with the phytoplankton Emiliania huxleyi demonstrate a several-fold increase in the production of DMS in the presence of herbivores (Wolfe and Steinke, 1996) or during viral infection (Evans et al., 2007). As far as we are aware, a description of functional diversity with volatilomic data has not been attempted, but it is likely that volatilomic data can assist with explaining and predicting the impact of organisms on ecosystems.
5. APPLICATION OF VOLATILOMICS TO ECOLOGICAL RESEARCH: USING VOLATILOMICS TO ‘DIRECT’ ENVIRONMENTAL MANAGEMENT Volatilomes are currently used as biomarkers to detect food spoilage (Mayr et al., 2003) and human diseases (see Section 3). Early monitoring
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systems detecting stressors on aquatic ecosystems could provide signals or early warning prior to detrimental changes in the environment or changes to food web structures due to biodiversity loss or invasions. This could assist with identifying effective management strategies for degraded and/or fragile ecosystems and industrial processes. Comparing the patterns of BVOCs between healthy and unhealthy systems, for example in seaweed mariculture or salmon farming, would likely identify volatile biomarkers that are sensitive to changes in temperature, the infestation with parasites or infectious diseases (Saha et al., 2018). Our own pilot studies with seaweeds showed that BVOC profiles change during microbial infection that can harm and degrade seaweeds (data not shown). Many conventional tools to assess such changes including PAM-fluorometry or molecular characterisation of the genetic diversity are relatively slow and expensive, and require bench-top equipment and skilled personnel. This makes them unsuited for rapid field-testing and not amenable to automation so that real-time monitoring tools for predicting the state of health of marine organisms or systems are lacking. We suggest that volatilomes should be assessed for such monitoring tasks with the aim of developing simple miniaturised sensor systems to monitor selected biomarkers specific to the organism, community or environment under investigation. This is analogous to many proposed methods for deploying biomonitors for rapid biodiversity assessment based on next-generation sequencing (NGS) technologies (e.g. Bohan et al., 2017), which provide data that can be used to reconstruct entire ecological networks to assess ecosystem health (Derocles et al., 2018). The advantage of volatilomics over these NGS-based methods is that it has the potential to be deployed in real time, continuously tracking the volatilomic footprint, whereas NGS methods currently (and probably for the near future) rely on (an often time consuming) extraction/isolation of nucleic acids from environmental samples. This may make volatilomic biomonitoring more suitable than NGS approaches for certain environments; for example, highly dynamic ecosystems or those where rapid detection of changes (albeit at a potentially lower resolution of information) to the system health are required (e.g. aquaculture/mariculture). However, a potential disadvantage to volatilomic approaches is the associated informatics required to process data are far less well developed than the bioinformatics methods available for NGS analysis, although with very similar analytical needs and a huge potential for sharing knowledge and adapting other ‘omics’ informatics approaches. Any comparison of volatilome samples based on chromatography must denoise data and provide a base-line correction, account for
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retention drift and correctly align the chromatogram traces from different samples so that statistical comparisons are comparing equivalent data, pick peaks from the chromatograms and merge the resulting processed data across samples. Many methods already exist for all or part of this (e.g. Hoffmann et al., 2012; Zheng et al., 2017), but generally larger-scale pipelines for processing massive amounts of data are less well developed (for a review of current approaches, see Smolinska et al., 2014). However, dedicated analytical packages are emerging within the research community (e.g. Ottensmann et al., 2018) and as more follow, a rapid increase in the depth of volatilome research will not be far behind. These approaches allow comparisons of data without the need to specifically associate any single VOC signals to individual organisms, and once chromatograms are aligned and the data have been extracted, differences in sample profiles can be statistically evaluated using a standard suite of multivariate analyses and ordinations. However, volatilome analysis will always be strengthened if specific organisms (or metabolic processes) can be identified within the data. For this to happen, volatilome signatures need to be matched to those housed in appropriately curated databases, with identifying tags associated with each specific volatilome signature. This is analogous to the bioinformatic methods used to assign taxonomy or function to NGS data and suffers from the same main limitation—a lack of information, poor curation or incorrect information available in the databases. Arguably the biggest limitation currently is a lack of information on resolved (i.e. links between species and volatilome signatures are established) volatilome signatures from aquatic ecosystems and their organisms. However, this is beginning to change and various platformspecific databases exist for the identification of chromatographic and/or mass-spectrometric data including the National Institute of Standards and Technology (NIST) databases for mass-spectral data, a GC retention index collection and various freely available data analysis tools, or the METLIN metabolite database that provides different metabolite-searching tools. But as aforementioned, identification of metabolites is not strictly required to interpret volatilomes as demonstrated in Figs. 1 and 2—the key, as with all methods, is to provide the appropriate data for the particulare biomonitoring context being considered. In this instance, we believe the suggested volatilomic approach, in combination with established tools and techniques highlighted in Fig. 3, could be employed as a future method for the biomonitoring of aquatic ecosystems and provide an exceptional early warning system for rapid changes in response to environmental perturbations within these ecosystems.
Fig. 3 Can we use volatilomics as a stand-alone tool or in combination with other ‘omics’ techniques to characterise metabolic finger- and footprints in the assessment of the state of health of aquatic organisms (e.g. healthy vs. unhealthy seaweeds)? Figure adapted from Brodie, J., Ball, S.G., Bouget, F.-Y., Chan, C.X., De Clerck, O., Cock, J.M., Gachon, C., Grossman, A.R., Mock, T., Raven, J.A., Saha, M., Smith, A.G., Vardi, A., Yoon, H.S., Bhattacharya, D., 2017. Biotic interactions as drivers of algal origin and evolution. New Phytol. 216, 670–681.
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ACKNOWLEDGEMENTS Constructive comments from two anonymous reviewers improved an earlier version of the manuscript. Bettina Hodapp, Rameez Subhan and Dominik Martin-Creuzburg provided assistance with generating data for Fig. 1. The Konstanz-Essex Development Fund provided financial support to Michael Steinke and Dominik Martin-Creuzburg. Mahasweta Saha acknowledges financial support from the German Research Foundation (DFG) under Grant number SA 2571/2-1.
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Volatile Biomarkers for Aquatic Ecological Research Michael Steinke1, Luli Randell, Alex J. Dumbrell, Mahasweta Saha2 University of Essex, School of Biological Sciences, Colchester, United Kingdom 1 Corresponding author: e-mail address: [email protected]
Contents 1. Introduction 1.1 Volatilomes: The Volatile Subset of Metabolomes 1.2 Volatilomics: Terms and Techniques 2. Principal Techniques for Measuring Biogenic Volatiles 3. Medical Volatilomics Provides a Blueprint for Ecological Research 4. Role of Volatiles in Aquatic Ecological Interactions 4.1 Case Study: Volatilomes of Freshwater Phytoplankton 5. Application of Volatilomics to Ecological Research: Using Volatilomics to ‘Direct’ Environmental Management Acknowledgements References
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Abstract All organisms and ecosystems emit and consume volatile organic compounds (VOCs). Traditionally, these have been qualitatively and quantitatively described in isolation without full consideration of the ‘signatures’ produced by the totality of all volatiles released. Here, we suggest that volatilomics, a research area applied to medical diagnostics, soil biology and pest control, can advance aquatic ecological research by providing a relatively fast diagnostic tool to investigate, for example, taxonomic and likely also functional diversity in aquatic systems—providing a novel technique for the biomonitoring of aquatic environments. Our case study demonstrates the utility of volatilomics to differentiate between four different algal genera using a principal component analysis. We highlight the utility of volatilomics to the monitoring of environmental processes and discuss its application to inform industrial mariculture procedures. Keywords: Volatilomics, Volatile organic compounds (VOCs), Biomonitoring, Aquatic, Environmental management 2
Current address: GEOMAR Helmholtz Centre for Ocean Research Kiel, D€ usternbrooker Weg 20, 24105 Kiel, Germany.
Advances in Ecological Research, Volume 59 ISSN 0065-2504 https://doi.org/10.1016/bs.aecr.2018.09.002
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2018 Elsevier Ltd All rights reserved.
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1. INTRODUCTION Terrestrial and aquatic plants and animals produce volatile organic compounds (VOCs) that provide signals acting as chemical cues, playing central roles in the recognition, attraction and deterrence of different species. Aquatic organisms emit volatiles when exposed to air or submerged underwater for intra- and interspecific communication (Mollo et al., 2017; Saha et al., 2018). ‘Volatilomics’ refers to the scientific study of volatile metabolites and, although relatively new in the ‘omics’ arena, is increasingly appreciated as a tool for the noninvasive presymptomatic diagnosis of human health (Amann and Smith, 2013). This has resulted in new methods for the early detection of various diseases in humans and can critically assist with identifying suitable strategies in the management of human health. Although technically feasible, similar applications have not yet been developed in aquatic research, yet these have the potential to be employed as a noninvasive, nondestructive, rapid and potentially highly cost-effective tool for biomonitoring, allowing users to readily survey the health status of marine and freshwater organisms and their environments. Given the embryonic stage of volatilomics research in being applied to aquatic biomonitoring, here we provide a brief overview of this developing technique and discuss the utility of aquatic volatilomics as a tool to advance freshwater and marine ecological research.
1.1 Volatilomes: The Volatile Subset of Metabolomes Every organism produces metabolites that provide unique chemical footprints. The scientific study of these metabolites is referred to as metabolomics and aims to describe the diversity and abundance of metabolites in samples of differing complexities of their taxonomic composition, inter- and intraspecific interactions (e.g. sexual attraction, predator deterrence) or sampled environment (e.g. laboratory culture, microcosm or ecosystem). Hence, metabolomics offers a powerful approach to represent the link between genotype and molecular phenotype, and can be used to elucidate functions from the organism to the ecosystem scale (Goulitquer et al., 2012). For example, specific metabolomic signatures can be used to reveal both an organism’s presence and its metabolic activity in the environment (Lee et al., 2012). The ‘volatilome’ represents the volatile subset of the metabolome, and its study finds application for the development of noninvasive biomarkers in the
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clinical diagnosis and monitoring of human diseases (Amann and Smith, 2013). It is also used in pest control and yield improvement in agricultural systems (Pickett and Khan, 2016; Shrivastava et al., 2010) and sometimes focuses on the microbial production of volatiles in soils to characterise the function of plants, fungi and bacteria (Insam and Seewald, 2010; Kanchiswamy et al., 2015; Redeker et al., 2018).
1.2 Volatilomics: Terms and Techniques Various terms are used to describe and classify volatiles (Achyuthan et al., 2017), and examples are presented in Table 1. Compounds in these classes are characterised by a relatively high vapour pressure at room temperature so that molecules of volatile compounds have a large tendency to escape from their liquid or solid form into the gas phase. Terms used to describe (components of ) the volatilome include volatile inorganic compounds (VICs), biogenic volatile inorganic compounds (BVICs), volatile organic compounds (VOCs), nonmethane volatile organic compounds (NMVOCs), biogenic volatile organic compounds (BVOCs), microbial volatile organic compounds (MVOCs), oxygenated volatile organic compounds (OVOCs), hydrocarbons (HCs) and nonmethane hydrocarbons (NMHCs). There is a confusing use of terms in the literature that describe volatiles released in biological processes. The three principle terms ‘volatomics’, ‘volatolomics’ and ‘volatilomics’ (and their derivatives ‘volatome’, ‘volatolome’ and ‘volatilome’) are currently used in the literature to describe the study of volatiles produced in biological processes. A search on the Web of Science for ‘volatom*’, ‘volatolom*’ and ‘volatilom*’ showed that the terms volatilomics and volatilome are most widely used in the published literature (559 citing articles, h-index of 12; 18 July 2018). However, here we briefly consider the meaning and definitions of these neologisms in an attempt to avoid possible confusion and encourage a coherent use of appropriate terms in the future. The ‘volatome’ is defined as ‘… the sum of all released volatile organic compounds (VOCs) over a specific time and space…’ (D’Alessandro, 2006). By this definition, the term excludes inorganic volatiles (e.g. H2S) that may add important diagnostic potential when studying the trace gas biology of aquatic environments. Furthermore, from a linguistic point of view, ‘volatomics’ may be confused with ‘something to do with atoms’ and we suggest that volatomics may be not the most appropriate term to use when describing the release of volatiles from biological systems.
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Table 1 Classes of Volatiles With Typical Abbreviations, Their Definition and Example Compounds Typical Class Abbreviation Definition Compound(s) References
Volatile inorganic compound
VIC
Carbon-free compound that has a high vapour pressure at room temperature
Argon, helium, Kennes et al. nitrogen, hydrogen (2016) sulphide, ammonia
Biogenic volatile inorganic compound
BVIC
Volatile inorganic compound produced directly but not exclusively from biological processes
Nitrogen, hydrogen sulphide, ammonia
Kennes et al. (2016)
Volatile organic compounds
VOC
Carbon-containing compound that has a high vapour pressure at room temperature
Methane, acetone, dimethyl sulphide
Krupa and Fries (1971)
Nonmethane NMVOC volatile organic compound
All VOCs but excluding methane
Acetone, dimethyl sulphide
Anastasi et al. (1991)
Biogenic volatile organic compounds
BVOC
Volatile organic compound produced directly but not exclusively from biological processes
Hydrogen sulphide, dimethyl sulphide, isoprene, ethene
Geron et al. (1994)
Microbial volatile organic compounds
MVOC
Volatile organic compound formed in the metabolism of fungi and bacteria (preferentially used in soil ecology literature)
Geosmin, dimethyl disulphide, hydrogen cyanide
Korpi et al. (2009)
Oxygenated volatile organic compounds
OVOC
Variety of volatile organic compound with oxygenated side groups
Methanol, acetone
Heikes et al. (2002)
Hydrocarbons HC
Organic compound consisting entirely of hydrogen and carbon
Methane, pentane, ethene
Nonmethane NMHC hydrocarbons
All hydrocarbons excluding methane
Butane, isoprene
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As far as we are aware, a clear definition of ‘volatolome’ is lacking in the literature. The term suggests a closer relationship with ‘metabolome’ and may be the most appropriate term when specifically addressing the volatile metabolome, i.e., the volatile subset of organic and inorganic metabolites produced by the collective metabolism(s) of organism(s), communities or entire ecosystems. It is important to note that a volatolome is the net result of volatile production and consumption processes. The term ‘volatilome’ includes the volatolome plus nonbiogenic, exogenously derived compounds that do not stem from metabolic processes (e.g. environmental contaminants; Insam and Seewald, 2010). For example, this term is often used in the medical study of human breath where the volatile composition is influenced by the combination of metabolically produced gases and volatiles exchanged with the atmosphere or ingested with food. Therefore, volatilome may be the best general term to use when the metabolic processes leading to the production of volatiles are unknown or poorly characterised. Measurements of the volatilome can provide an in vivo metabolic footprint of the entirety of volatiles released without sample pretreatment or extraction (Insam and Seewald, 2010). In many settings, modern volatilomics may be superior to other biomarkers since it offers the possibility for an immediate, continuous (online) measurement which applies, for example, to quality control (e.g. food spoilage, mould detection; Mayr et al., 2003), biodiversity assessment (e.g. herbivore detection; Miresmailli et al., 2010) and medical diagnostics (see Section 3). It is also clear that individual components in environmental volatilomes are of value as informationconveying chemicals (infochemicals) in ecological research (see Section 4). This is because they are central to regulating (i) individuals’ movement and behaviour, (ii) ecological interactions between and across populations and (iii) have the potential to affect the complexity of trophic structure in marine food webs (Nevitt et al., 1995; Steinke et al., 2002b, 2006).
2. PRINCIPAL TECHNIQUES FOR MEASURING BIOGENIC VOLATILES A number of techniques used in volatilomic analyses have recently been reviewed by Achyuthan et al. (2017). Most analyses use a type of gas chromatography (GC) coupled to various detectors including, for example, mass spectrometric detection (GC-MS; Hopkins et al., 2010), flameionisation detection (GC-FID; Steinke et al., 2018), flame-photometric
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detection (GC-FPD; Steinke et al., 2002a), photoionisation detection (GC-PID; Zhou et al., 2013) or Fourier transform infrared spectroscopy (GC-FTIR; Ketola et al., 2006). These techniques can be further combined with microextraction techniques including headspace solid-phase microextraction (HS-SPME; Vogt et al., 2008) or direct-immersion solid-phase microextraction (DI-SPME; Zhang et al., 2018) and may be extended to include two-dimensional separation techniques (e.g. GC GC-TOF MS; Phillips et al., 2013). GC techniques use various column materials to separate individual volatiles in often complex mixtures, and the choice of column material and methods in general is selective for particular groups of volatiles. This suggests (i) that the choice of methodological approach strongly affects possible outcomes of a study and (ii) that GC analysis is typically incapable of identifying and quantifying the total volatilome. Other spectrometric methods may overcome such limitations: for example, proton-transfer reaction mass spectrometry (PTR-MS; Halsey et al., 2017; Mayr et al., 2003) and chemical ionisation mass spectrometry (CIMS; Hopkins et al., 2016) have sufficient sensitivity to allow continuous monitoring (online measurement) of selected gas samples.
3. MEDICAL VOLATILOMICS PROVIDES A BLUEPRINT FOR ECOLOGICAL RESEARCH Research in medical volatilomics has greatly advanced the application of biogenic volatiles for the rapid and noninvasive diagnosis of early bacterial infections and genetic disorders including cancer or Alzheimer’s disease (Amann et al., 2014). Since metabolic reactions in cells, tissues or fluids result in the formation of volatiles, physiological abnormalities can be detected using methods that identify and quantify volatiles in urine, faeces or exhaled breath samples. For example, GC-MS analysis of urine samples demonstrates unique volatile compounds in transgenic mice with mutations on the amyloid precursor protein gene involved in the onset of Alzheimer’s disease (Kimball et al., 2016). In patients suffering from cystic fibrosis (CF), secondary infections with Pseudomonas aeruginosa alter microbial diversity and produce a change in a patient’s breath volatilome. This offers monitoring opportunities for airway infection as a responsive tool to reduce mortality rates in CF patients (Robroeks et al., 2010). HS-SPME coupled to GC-MS identified six compounds (2-pentanone, 2-heptanone, 3-methyl-3-buten-1-ol, ethyl acetate, ethyl propanoate and 2-methyl butanoate) that were only found in the headspace of cancerous cell lines (Silva et al., 2017). This knowledge can inform the development of novel technologies including GC-MS coupled
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to gold nanoparticle sensor arrays to distinguish between breath samples of cancer patients and healthy control groups (Peng et al., 2010). We suggest that much of the existing technology used in medical research can readily inform the investigation of ecological processes. Volatile-mediated signalling in plants already finds application in agriculture (Pickett and Khan, 2016), whereas ecological research using volatiles in aquatic environments is in its infancy (Saha et al., 2018). For example, identifying substantial differences in the quantitative and/or qualitative differences in volatilomes of ecosystems before and during disturbance would accelerate developing robust sensor technology for ecosystem biomonitoring. This could provide novel fast, sensitive and relatively inexpensive tools to alert of an onset of disturbance including infestation with parasites (e.g. salmon lice infection in finfish mariculture) or the changes associated with the deterioration of symbiotic relationships (e.g. before and during coral bleaching). Such early warning would provide avenues for new management strategies that prevent or reduce the degradation of ecosystems. For example, early detection of salmon lice may minimise the geographic spread of infestations so that costly and environmentally damaging treatments can be optimised. Another example application could allow for combinations of coral and their symbionts to be manipulated and tested in coral restoration projects to identify holobionts with greater resistance to stress responses triggered by environmental change.
4. ROLE OF VOLATILES IN AQUATIC ECOLOGICAL INTERACTIONS Volatiles shape a myriad of species interactions on land and critical ecological processes that depend on smell are commonplace (Ache and Young, 2005; Kessler and Baldwin, 2001). For example, volatiles often facilitate communication through their role in intra- and interspecific signalling in bacteria and fungi (e.g. hydrogen cyanide produced in bacteria; Piechulla et al., 2017), plants (e.g. the gaseous hormone ethylene; Wang et al., 2002), insects (e.g. trail-marking in ants, sex pheromones in moths; Wyatt, 2014) and vertebrates including humans (Nevitt, 2008; Wallraff, 2004; Wyatt, 2014). In contrast to the terrestrial environment, our understanding of volatilemediated processes in aquatic environments is limited, with previous studies restricted to a relatively small number of infochemicals (Giordano et al., 2017; Hay, 2009; Moelzner and Fink, 2015; Pohnert et al., 2007). This is surprising since aquatic environments are of particular relevance for volatile-mediated infochemistry with diffusion typically four orders of
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magnitude slower in water than in air. Furthermore, numerous microbial consumption processes, as exemplified for the abundant trace gases dimethyl sulphide (DMS; Sch€afer et al., 2010) and isoprene (Alvarez et al., 2009), rapidly decrease background concentrations and substantially enhance the directional quality of chemical gradients providing the basis of efficient chemical communication. Hence, diffusive volatiles are among the most important parts of the ‘chemical language’ in the infochemistry of any aquatic organism as demonstrated by the identification of the volatile sexual pheromone ectocarpene in the seaweed Ectocarpus siliculosus about 30 years ago (Maier and M€ uller, 1986). Aquatic volatiles can also transfer into the atmosphere to communicate food web interactions over relatively long distances (Nevitt et al., 1995; Savoca and Nevitt, 2014) and affect atmospheric processes and climate (Carpenter et al., 2012). For example, DMS, isoprene and numerous halocarbons are climatically important gases that have received global research attention and have been studied for their importance to ecological (Steinke et al., 2006), biogeochemical (Hopkins et al., 2010) and climate science (Vallina and Simo´, 2007). Albatrosses and petrels rely on the volatile sulphur gas DMS to track highly productive areas (Nevitt, 2008), and brown seaweeds release volatile pheromones (Pohnert and Boland, 2002). Volatiles also react quickly in response to abiotic conditions and climate stressors (Pen˜uelas and Staudt, 2010). For example, the production of toxic cyanogen bromide (BrCN) by the microalgae Nitzschia cf. pellucida that kills surrounding biofilm organisms is light dependent with a short burst of BrCN immediately after sunrise (Vanelslander et al., 2012). Furthermore, different VOCs are emitted by healthy, senescent or stressed cells, apoptotic tissue or prey under predator attack (Achyuthan et al., 2017). These evidences provide us with a ‘smoking gun’ and make VOCs strong candidates for the interrogation of the state of health of aquatic organisms or communities, and thus the quantification of the volatilome as an ideal target for longer-term biomonitoring of aquatic ecosystems. To explore the basic functionality of volatilomics for biodiversity research, we reanalysed a dataset on freshwater volatiles and demonstrate proof of principle.
4.1 Case Study: Volatilomes of Freshwater Phytoplankton We previously conducted measurements of isoprene and DMS in unialgal cultures of freshwater phytoplankton (Steinke et al., 2018). Using the raw gas chromatographic data, we explored the diversity of algal volatilomes
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in four species from different taxonomic classes: Chlamydomonas reinhardtii (Chlorophyceae), Cryptomonas sp. (Cryptophyceae), Cyclotella meneghiniana (Bacillariophyceae) and Aphanizomenon flos-aquae (Cyanophyceae). Method details are described in Steinke et al. (2018). Briefly, algal cultures were grown under constant conditions using culture-specific media, aliquoted into glass bottles that were closed with gas-tight glass stoppers (Winkler bottles) and incubated under growth conditions for approximately 4 h. At the end of the incubation, media were purged with N2 gas at 80 mL/min and this sample gas stream was cryogenically enriched at 150°C before analysis using gas chromatography with flame ionisation detection (GC-FID). The diversity and abundance of peaks on the chromatograms indicated speciesspecific release of volatiles from the cultures (Fig. 1). The chromatogram traces were manually aligned for comparison, and the data deconstructed into a multivariate matrix on which principal component analysis (PCA) was applied to identify distinct data clusters associated with the four algal genera (Fig. 2). With this limited dataset it may be premature to attempt an in-depth assessment of the suitability of volatilomics for algal research. For example, it remains to be tested whether the quantity of volatiles is directly proportional to algal biomass and how fluctuations in environmental conditions may affect the quantity and quality (plasticity) of volatilomes to identify robust volatile biomarkers for species of interest. Furthermore, it is also possible that composition and age of algal media may affect the outcome of volatilomic analyses. To counter this, algal media should be taken as controls to eliminate background noise. Nevertheless, the data suggest that the quantification of volatiles could be explored for quality control in algal biotechnology and may be useful in the assessment of biodiversity in complex mixtures and environmental samples. It is important to note that environmental volatilomics should not be confused with an overly simplistic ‘one signature compound per species or function’ approach but uses the entirety of the volatile footprint to inform how we describe biodiversity. The separation of thousands of volatile compounds is already feasible in human breath analysis (e.g. Phillips et al., 2013), and tens of thousands of molecular formulae can be identified in complex natural organic matter using Fourier transform ion cyclotron resonance mass spectra (FT-ICR-MS; Riedel and Dittmar, 2014). As already applied to other omics approaches, a volatilomic assessment of complex environmental samples would require the collection of large datasets in publicly accessible databases, preferentially using unified methodological approaches (see Section 5).
Fig. 1 Overlays of typical chromatograms for volatile organic compounds (VOC) produced from similar biomass in freshwater phytoplankton. (A) Overlay of data for Cyclotella sp. (brown line) and instrument blank (grey line) showing numerous signature peaks. Grey rectangles indicate position of timeshots in lower panel. (B–D) Overlays showing examples of selected signature volatiles in four species that illustrate the rich signature qualities for the identification of specific genera (Chlamydomonas (green line), Cyclotella (brown line), Cryptomonas (red line), Aphanizomenon (blue-green line)), based on their volatile profiles. Grey line shows instrument blank.
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Principal component dimension two (18%)
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Aphanizomenon sp. Cyclotella sp.
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Chlamydomonas sp. Cryptomonas sp. 0 5 −5 Principal component dimension one (27%)
Fig. 2 Principal component analysis (PCA) of volatilomes from four specific genera (Chlamydomonas, Cyclotella, Cryptomonas, Aphanizomenon) demonstrates clear separation based on taxonomic identity of the unialgal cultures.
Once the volatilome patterns of major components to biodiversity are known, it will become possible to search for such patterns and identify key volatilome components for possible monitoring purposes. For example, if we demonstrate that harmful algal bloom species produce typical volatile footprints, a network of simple sensors that target a low number of volatiles may inform established but costly routine sampling that uses either microscopic enumeration or genetic identification of harmful species. Our case study illustrates the utility of volatilomics to investigate taxonomic diversity; however, the production of volatiles is also affected by functional diversity. Microcosm experiments with the phytoplankton Emiliania huxleyi demonstrate a several-fold increase in the production of DMS in the presence of herbivores (Wolfe and Steinke, 1996) or during viral infection (Evans et al., 2007). As far as we are aware, a description of functional diversity with volatilomic data has not been attempted, but it is likely that volatilomic data can assist with explaining and predicting the impact of organisms on ecosystems.
5. APPLICATION OF VOLATILOMICS TO ECOLOGICAL RESEARCH: USING VOLATILOMICS TO ‘DIRECT’ ENVIRONMENTAL MANAGEMENT Volatilomes are currently used as biomarkers to detect food spoilage (Mayr et al., 2003) and human diseases (see Section 3). Early monitoring
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systems detecting stressors on aquatic ecosystems could provide signals or early warning prior to detrimental changes in the environment or changes to food web structures due to biodiversity loss or invasions. This could assist with identifying effective management strategies for degraded and/or fragile ecosystems and industrial processes. Comparing the patterns of BVOCs between healthy and unhealthy systems, for example in seaweed mariculture or salmon farming, would likely identify volatile biomarkers that are sensitive to changes in temperature, the infestation with parasites or infectious diseases (Saha et al., 2018). Our own pilot studies with seaweeds showed that BVOC profiles change during microbial infection that can harm and degrade seaweeds (data not shown). Many conventional tools to assess such changes including PAM-fluorometry or molecular characterisation of the genetic diversity are relatively slow and expensive, and require bench-top equipment and skilled personnel. This makes them unsuited for rapid field-testing and not amenable to automation so that real-time monitoring tools for predicting the state of health of marine organisms or systems are lacking. We suggest that volatilomes should be assessed for such monitoring tasks with the aim of developing simple miniaturised sensor systems to monitor selected biomarkers specific to the organism, community or environment under investigation. This is analogous to many proposed methods for deploying biomonitors for rapid biodiversity assessment based on next-generation sequencing (NGS) technologies (e.g. Bohan et al., 2017), which provide data that can be used to reconstruct entire ecological networks to assess ecosystem health (Derocles et al., 2018). The advantage of volatilomics over these NGS-based methods is that it has the potential to be deployed in real time, continuously tracking the volatilomic footprint, whereas NGS methods currently (and probably for the near future) rely on (an often time consuming) extraction/isolation of nucleic acids from environmental samples. This may make volatilomic biomonitoring more suitable than NGS approaches for certain environments; for example, highly dynamic ecosystems or those where rapid detection of changes (albeit at a potentially lower resolution of information) to the system health are required (e.g. aquaculture/mariculture). However, a potential disadvantage to volatilomic approaches is the associated informatics required to process data are far less well developed than the bioinformatics methods available for NGS analysis, although with very similar analytical needs and a huge potential for sharing knowledge and adapting other ‘omics’ informatics approaches. Any comparison of volatilome samples based on chromatography must denoise data and provide a base-line correction, account for
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retention drift and correctly align the chromatogram traces from different samples so that statistical comparisons are comparing equivalent data, pick peaks from the chromatograms and merge the resulting processed data across samples. Many methods already exist for all or part of this (e.g. Hoffmann et al., 2012; Zheng et al., 2017), but generally larger-scale pipelines for processing massive amounts of data are less well developed (for a review of current approaches, see Smolinska et al., 2014). However, dedicated analytical packages are emerging within the research community (e.g. Ottensmann et al., 2018) and as more follow, a rapid increase in the depth of volatilome research will not be far behind. These approaches allow comparisons of data without the need to specifically associate any single VOC signals to individual organisms, and once chromatograms are aligned and the data have been extracted, differences in sample profiles can be statistically evaluated using a standard suite of multivariate analyses and ordinations. However, volatilome analysis will always be strengthened if specific organisms (or metabolic processes) can be identified within the data. For this to happen, volatilome signatures need to be matched to those housed in appropriately curated databases, with identifying tags associated with each specific volatilome signature. This is analogous to the bioinformatic methods used to assign taxonomy or function to NGS data and suffers from the same main limitation—a lack of information, poor curation or incorrect information available in the databases. Arguably the biggest limitation currently is a lack of information on resolved (i.e. links between species and volatilome signatures are established) volatilome signatures from aquatic ecosystems and their organisms. However, this is beginning to change and various platformspecific databases exist for the identification of chromatographic and/or mass-spectrometric data including the National Institute of Standards and Technology (NIST) databases for mass-spectral data, a GC retention index collection and various freely available data analysis tools, or the METLIN metabolite database that provides different metabolite-searching tools. But as aforementioned, identification of metabolites is not strictly required to interpret volatilomes as demonstrated in Figs. 1 and 2—the key, as with all methods, is to provide the appropriate data for the particulare biomonitoring context being considered. In this instance, we believe the suggested volatilomic approach, in combination with established tools and techniques highlighted in Fig. 3, could be employed as a future method for the biomonitoring of aquatic ecosystems and provide an exceptional early warning system for rapid changes in response to environmental perturbations within these ecosystems.
Fig. 3 Can we use volatilomics as a stand-alone tool or in combination with other ‘omics’ techniques to characterise metabolic finger- and footprints in the assessment of the state of health of aquatic organisms (e.g. healthy vs. unhealthy seaweeds)? Figure adapted from Brodie, J., Ball, S.G., Bouget, F.-Y., Chan, C.X., De Clerck, O., Cock, J.M., Gachon, C., Grossman, A.R., Mock, T., Raven, J.A., Saha, M., Smith, A.G., Vardi, A., Yoon, H.S., Bhattacharya, D., 2017. Biotic interactions as drivers of algal origin and evolution. New Phytol. 216, 670–681.
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ACKNOWLEDGEMENTS Constructive comments from two anonymous reviewers improved an earlier version of the manuscript. Bettina Hodapp, Rameez Subhan and Dominik Martin-Creuzburg provided assistance with generating data for Fig. 1. The Konstanz-Essex Development Fund provided financial support to Michael Steinke and Dominik Martin-Creuzburg. Mahasweta Saha acknowledges financial support from the German Research Foundation (DFG) under Grant number SA 2571/2-1.
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